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#artificial intelligence Preprint Sep 2026

HARMONIA: Interpretable Graph Learning through Mixtures of Neural Bases

Existing interpretable graph additive models still face limitations in either computational scalability or modeling flexibility. In terms of structural modeling, previous approaches either face quadratic scaling costs or sacrifice explicit source-to-target contribution decomposition. In terms of feature components, the...

Quan D. Bui, D. Nguyen, A. Dang et al. · 0 citations
#machine learning Preprint Sep 2026

RAPTOR: Role-Aware Private Training for Mixture-of-Experts

This work introduces RAPTOR - a Role-Aware Private Training framework, which alternates shared and expert optimization and targets each failure directly, using expert-specific clipping and noise together with a public expected-owner denominator and a count-independent update schedule that avoids conditioning on private...

Duc Dm, Khai Le-Duc, D. Nguyen et al. · 0 citations

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